The basic function of a communication system is to send information over a communication channel from a source that generates the information to one or more destinations. In a digital communication system, the information is converted into a digital format and then transmitted over the communication channel. The transmission of digital information is subject to the adverse effects of the communication channel, such as co-channel and adjacent channel interference, noise, dispersion, and fading. These effects introduce errors into the transmitted data stream. These effects are particularly severe in a radio communication system.
In 1948, Claude E. Shannon demonstrated in a landmark paper that proper encoding of the digital information prior to transmission may reduce the errors introduced by a noisy channel to any desired level. Encoding is the process of adding redundancy to information prior to its transmission so that errors which may occur during transmission can be detected and/or corrected. At the receiving end, the decoder makes use of the redundant information and a priori knowledge of the coding scheme to detect and/or correct errors that may have occurred during transmission.
Many types of error correction codes have been devised, including block codes and convolutional codes. Turbo codes, which were introduced in 1993, are considered to have high error correction capability and good performance and have been selected for use in third and fourth generation mobile communication systems. In its most basic form, a turbo code comprises two parallel systematic convolutional encoders connected in parallel by an interleaver. The first encoder operates on the original input bits and generates first parity bits. The interleaver permutes the order of the input bits and the interleaved bits are supplied to the second encoder. The second encoder operates on the interleaved bits output by the interleaver and generates second parity bits. For each input bit, three output bits are generated: the input (systematic) bit and two parity bits. A puncturing or rate-matching processor may be used following the encoder to select part of these output bits for transmission.
Iterative decoding is used at the receiver to decode turbo codes. A basic turbo decoder comprises two soft-input, soft-output (SISO) decoders connected in series by an interleaver. The received bits (or soft values obtained from the Rake or G-Rake receiver) are input to the first decoder. The first decoder uses the received bits (or soft values) and extrinsic information fed back from the second decoder to generate a soft estimate of the original input bits expressed as a log-likelihood ratio and extrinsic information that is supplied to the second decoder. The extrinsic information output from the first decoder is interleaved before it is input to the second decoder to compensate for the interleaving performed at the encoder. The second decoder generates a more refined log-likelihood estimate of the original input bit and extrinsic information that is fed back to the first decoder. This process repeats for a number of iterations. A final decision about the bit is made by hard limiting the soft estimate output by the second decoder.
The demand for and increasing popularity of broadband wireless communications have driven communication systems designers to seek better and better performance. Receiver technologies in particular play an important role in advancing wireless system performance; these technologies continue to evolve.
In Wideband Code-Division Multiple Access (W-CDMA) systems, for example, Rake receivers were first introduced, and then significant performance improvements over the conventional Rake receiver were achieved with the introduction of linear equalization (e.g., in the so-called G-Rake receiver). However, as the data rate is pushed even higher through the use of higher order modulation and/or multiple-input multiple-output (MIMO) techniques, good performance with linear equalization is becoming more and more difficult to achieve, especially in dispersive channels.
There are a number of known approaches to improve performance beyond that of linear equalization. For example, U.S. patent application Ser. No. 12/035,846, titled “Method and Apparatus for Block-Based Signal Demodulation” and filed 22 Feb. 2008, by Bottomley, et al. (hereinafter “the BDFE application”), describes a block decision-feedback equalizer (BDFE), in which block-based equalization (linear equalization or decision feedback equalization) is used to suppress inter-block interference and to produce detection statistics for the symbols in a symbol block. Joint detection addresses intra-block interference by jointly detecting the most likely combination of symbols within the symbol block, based on the corresponding detection statistics. U.S. Patent Application Publication 2007/0147481, titled “Linear Turbo Equalization Using Despread Values” and filed 22 Dec. 2005 by Bottomley et al. (hereinafter “the Linear Turbo Equalization publication”), describes linear turbo equalization (TE), which is a linear equalizer, based on a generalized-Rake (G-Rake) receiver design, that uses decoder feedback in forming Rake combining weights as well as in forming a self-interference estimate removed from the equalizer signal provided to the decoder. Both the BDFE application and the Linear Turbo Equalization publication are incorporated herein by reference, in their entireties. Various techniques involving successive interference cancellation (SIC) are also known, and are being further developed for use in advanced receivers.
All of these receiver technologies benefit from so-called soft subtraction, in which a soft-value of an interfering symbol can be derived and used in an interference cancellation process. Such soft values can be derived prior to decoding (pre-decoding soft values) or after decoding (post-decoding) a series of estimated symbol values. The former approach is more suitable for block decision-feedback equalization, while the latter is more suitable for linear turbo equalization. Successive interference cancellation techniques can be based on either pre-decoding or post-decoding soft subtraction.
Performance gains from soft subtraction can be substantial. In one study, multi-user detection (MUD) processes based on hard subtraction and soft subtraction were compared for a number of scenarios. With soft subtraction, better multi-user detection performance is consistently achieved, which in return reduces the required received power per user. As a result, the system's aggregate rise-over-thermal is also reduced. In high-data rate scenarios, approximately 1 to 2 dB gain is expected to result from the use of soft subtraction.